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Simulation of Long-Term Water Erosion in Rainfed Croplands of Eastern Washington
Mugal Samrat Dahal1,*, Joan Wu1, Mariana Dobre2, Robert P. Ewing3
Published in Journal of the ASABE 67(1): 193-206 (doi: 10.13031/ja.15623). Copyright 2024 American Society of Agricultural and Biological Engineers.
1Department of Biological Systems Engineering, Washington State University, Puyallup, Washington, USA.
Submitted for review on 5 April 2023 as manuscript number NRES 15623; approved for publication as a Research Article and as part of the Soil Erosion Research Symposium Collection by Associate Editor Dr. Zhiming Qi and Community Editor Dr. Kyle Mankin of the Natural Resources & Environmental Systems Community of ASABE on 21 November 2023.
Highlights
Abstract. Water erosion is an ongoing problem in eastern Washington due to its hilly terrain, highly erodible silt loam soils, rain on thawing soil, and the prevalence of conventional tillage. The region is characterized by a Mediterranean-type climate with warm, dry summers and cool, wet winters. Three distinct precipitation zones, with annual totals low (<380 mm), intermediate (380–460 mm), and high (>460 mm), dictate the area’s crop rotations. A unique 43-year (1940–1982) dataset of winter erosion measured on multiple agricultural fields in Whitman County, eastern Washington, by Verle Kaiser, a USDA Soil Conservation Service agronomist, showed annual erosion rates averaging 53.8 Mg ha-1, far exceeding the current Natural Resources Conservation Service tolerable limit of 11 Mg ha-1 yr–1 for the soils in the area. Kaiser’s field data allowed us to compare the historical field-measured erosion rates with those simulated by the WEPP (Water Erosion Prediction Project) model. Anthropogenic factors, such as tillage and crop rotation, change with time. Conservation tillage, including reduced- and no-till, has been increasingly adopted in eastern Washington since the mid-1980s. The specific objectives of this study were to (1) apply the WEPP model to simulate soil erosion in eastern Washington and evaluate the interactive effects of climate and management, in addition to topography and soil, on water erosion in the study area, and (2) compare the simulation results with Kaiser’s historical field dataset and elucidate the long-term soil erosion trend. The WEPPcloud interface was used to delineate a watershed within each precipitation zone of the study area. Climate inputs were divided into two periods: the past (1939–1982) and the present (1983–2020). Erosion has noticeably decreased from the past to the present, with WEPP simulated annual erosion averaging 13.5, 34.5, and 52.6 Mg ha-1 for the past, and 9.5, 14.1, and 15.5 Mg ha-1 for the present, in the selected watersheds in the low-, intermediate-, and high-precipitation zones, respectively. The decreasing trend was primarily due to the increased adoption of conservation tillage and crop rotation, as well as a decrease in the number of high-intensity precipitation events in the present climate.
In the inland Pacific Northwest (PNW), water erosion has been a significant threat to agricultural production and the environment since the area’s large-scale wheat production began in the early 1880s (USDA, 1978). Major factors of erosion in this area include hilly topography, wet winters with numerous freeze-thaw cycles, rain on thawing soil, silt loam soil prone to erosion, and conventional tillage that pulverizes the soil and leaves it bare (Papendick et al., 1995; Greer et al., 2006). Annual precipitation varies across the region, with distinct annual precipitation zones from west to east: low (<380 mm), intermediate (380–460 mm), and high (>460 mm). Highly variable erosion rates across the area have been reported (Kok et al., 2009; Dahal et al., 2022).
Multiple studies have been carried out to assess water erosion in the area. The USDA (1978) applied the USLE technique (Wischmeier and Smith, 1978) to estimate erosion for major soils and crop rotations of that time for all three precipitation zones of the Palouse River Basin, located in the southern part of the inland PNW. Average annual erosion rates were estimated as 29, 45, and 27 Mg ha-1 for the low-, intermediate-, and high-precipitation zones, far exceeding the NRCS’s soil loss tolerance level of 11 Mg ha-1 yr-1 (Soil Science Division Staff, 2017).
An extensive field assessment of soil erosion in Whitman County, WA, was conducted across 400,000 ha of cropland by USDA Soil Conservation Service agronomist Verle Kaiser from 1940–1982. The survey involved visually assessing soil loss due to different types of erosion (rill, gully, and soil-slip) for various “land capability classes” (USDA, 1978), which represent land types classified by their suitability for agricultural use (Klingebiel and Montgomery, 1961). The same fields were surveyed and sampled each spring, mainly within the intermediate- and high-precipitation zones (fig. 1). The Alutin method, which measures multiple cross-sectional areas of a rill, each along a transect, was used to quantify rill erosion. This investigation revealed a high annual erosion rate, averaging 53.8 Mg ha-1 (McCool and Roe, 2005). The erosion rate decreased from 1983 to 2004, possibly due to less severe weather conditions and an increase in conservation farming practices (McCool and Roe, 2005).
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| Figure 1. Model watersheds in Whitman County, Washington, delineated through the WEPPcloud interface (a), and close-ups of this study’s (b) low-, (c) intermediate-, and (d) high-precipitation watersheds. Kaiser Sites are the field sites denoted “Sites with a long history of observation” in the Kaiser study (Kaiser, 2021). |
A long-term experimental study was carried out by McCool et al. (2006) at the Palouse Conservation Field Station near Pullman, WA, in the high-precipitation zone. Water erosion from natural precipitation events was measured in multiple runoff plots from 1978 to 1991. Various crop rotations, including (1) continuous bare fallow and (2) winter wheat followed by one of winter wheat, summer fallow, spring peas, or another small grain, were implemented under either tilled or no-till conditions. The authors report annual erosion rates as high as 120.6 Mg ha-1 for continuous bare fallow, 18.2 Mg ha-1 for winter-wheat summer fallow for the tilled condition, and 0.12 Mg ha-1 for continuous winter wheat under no-till.
Kok et al. (2009) applied RUSLE2 (Renard et al., 1996; Foster et al., 2003) to simulate soil erosion for all three precipitation zones in the study area. The study was part of Solutions to Environmental and Economic Problems (STEEP), a USDA-funded project. For the years 1975, 1990, and 2005, corresponding to the beginning, middle, and end of STEEP, crop rotations and tillage practices typical of those periods were used in the simulation. The simulated average erosion rates were 20, 27, and 45 Mg ha-1 in 1975, 14, 16, and 24 Mg ha-1 in 1990, and 10, 13, and 11 Mg ha-1 in 2005 for the low-, intermediate-, and high-precipitation zones, respectively, showing a consistent decrease in erosion over the course of the STEEP project.
Dahal et al. (2022) simulated 30-year (1989–2018) water erosion for 12 counties of eastern Washington using the WEPP (Water Erosion Prediction Project; Flanagan and Nearing, 1995; Flanagan et al., 2007) model. Their purpose was to understand the interacting effects of soil, climate, topography, crop rotation, and tillage on erosion, and to assess the spatial distribution of erosion in the area. The authors classified slopes (5 classes) and soil (3 classes), and overlaid the combination with the three precipitation zones and their unique crop rotations. Using three levels of tillage (intense, reduced, and no-till), they simulated 135 (5×3×3×3) scenarios and obtained countywide erosion results by area-weighting for each county. They showed that steep slopes and shallow soils generally led to the highest erosion rates and reported the long-term average annual erosion in the low-, intermediate-, and high-precipitation zones as 3.8, 15.8, and 14.4 Mg ha-1. For Whitman County, simulated average annual erosion rates were 13.0, 19.0, and 15.4 Mg ha-1 in the aforementioned precipitation zones, with 59%, 57%, and 54% of the areas having erosion rates exceeding the current NRCS soil loss tolerance level of 11 Mg ha-1 yr-1. The erosion rates reported by Dahal et al. (2022) were slightly higher than those of Kok et al. (2009). In addition, the intermediate-precipitation zone generated the highest erosion rate in Dahal et al. (2022), while results were mixed in Kok et al. (2009).
Climate and management practices crucially influence soil erosion, causing considerable variation year-to-year (Li and Fang, 2016). Lin et al. (2020) assessed the temporal erosion trend during the period 1982–2015 in the semi-arid Hexi corridor of China using the integrated RUSLE-TLSD (Transport Limited Sediment Delivery; Renard et al., 1996) model. Their results showed remarkable variation in the annual erosion rate due to variations in precipitation patterns and changes in crop management and conservation practices. For instance, erosion rates in 2012 were 1.3–5.0 times higher than in other years, even though the total annual precipitation was not significantly higher than in other years. The high erosion rate was attributed to multiple heavy precipitation events with elevated erosive power, which was not experienced in other years. Similar temporal variation in erosion rates has been reported for eastern Washington (USDA, 1978; Ebbert and Roe, 1998; McCool and Roe, 2005; Dahal et al., 2022), with management practices (crop rotation, tillage) and variations in precipitation and temperature patterns being contributing factors (USDA, 1978; McCool and Roe, 2005; Dahal et al., 2022). A disproportionately large amount of erosion can occur in the wheat-planting portion of the crop rotation (Papendick et al., 1995; Dahal et al., 2022). The number of freeze-thaw cycles, the number of rain on thawing soil events, and the length of the frost period all vary year to year and can also cause fluctuations in annual erosion (McCool and Roe, 2005; McCool et al., 2011).
Erosion simulation models have proven to be a more cost-effective tool than field experimentation for assessing management scenarios over large study areas (Panagopoulos et al., 2015). WEPP, a continuous, process-based, distributed-parameter simulation model for hydrology and water erosion (Flanagan and Nearing, 1995), has been previously applied to evaluate the erosion effects of critical physical and management conditions, such as soil, topography, and various tillage practices (Williams et al., 2014; Brooks et al., 2015; Dahal et al., 2022).
Conservation practices have been adopted in the inland PNW since the mid-1980s (Kok, 2007; Dahal et al., 2022). Reduced tillage and annual cropping, instead of intensive tillage and fallow rotation, have been increasingly implemented by farmers in this region (Kok, 2007; Van Wie et al., 2013; Dahal et al., 2022). Assessing long-term erosion from the past to the present can help us understand the effects of changing management practices and climate processes that drive erosion. Therefore, the objectives of this study were to (1) apply the WEPP model to simulate soil erosion in eastern Washington and evaluate the interactive effects of climate and management, in addition to topography and soil, on water erosion in the study area, and (2) compare the simulation results with Kaiser’s historical field dataset and elucidate the long-term soil erosion trend.
Materials and Methods
Study Area
Whitman County was chosen as the study area because it has more area in cereal-grain production (3.0×105 ha; USDA NASS 2018) than any other county in the region. Numerous studies on water erosion have been conducted in the area, including the collection of long-term field erosion data by Verle Kaiser (McCool and Roe, 2005). The county, located in southeastern Washington, has elevation ranging from 160–1250 m a.m.s.l. (USGS, 2019; fig. 1). The climate in the area is Mediterranean, with dry summers and wet winters. The low-, intermediate-, and high-precipitation zones, respectively, have 355, 450, and 533 mm of long-term average annual precipitation (NCDC, 2022) and are classified as Dsb per the Köppen-Geiger system (Kottek et al., 2006).
Topography varies from flat to having a slope gradient greater than 45%, with steep slopes primarily present near the Snake River Canyon and in the high-precipitation zone and gentle slopes predominating in the low-precipitation zone. Soil texture is mainly silt loam, with most soils being either Mollisols formed from aeolian deposits or Andisols formed from volcanic ash (Papendick et al., 1995; Shepherd, 1985). Intense tillage with multiple operations and a winter wheat-summer fallow rotation were commonly used in the past. Since the mid-1980s, reduced- and no-till, together with crop rotations, have been increasingly adopted (McCool and Roe, 2005; Kok et al., 2007). Limited by available soil water, dominant crop rotations in the low-, intermediate-, and high-precipitation zones are respectively winter wheat (Triticum aestivum L.)-fallow, winter wheat-spring barley (Hordeum vulgare L.)-fallow, and winter wheat-spring barley-pea (Pisum sativum L.) (Kok et al., 2007). One HUC-12 watershed within each precipitation zone was selected for WEPP simulation: Winn Lake Canyon Watershed (WLCW, HUC170601080805, henceforth WLCW-Low), Upper Imbler Creek Watershed (UICW, HUC170601090403, UICW-Intermediate), and Spring Flat Creek Watershed (SFCW, HUC170601080209, SFCW-High) for the low-, intermediate-, and high-precipitation zone (fig. 1).
Climate Analyses
Daily inputs of precipitation and temperature were obtained from weather stations (NCDC, 2022) within each of the precipitation zones: Pullman 2 NW WA 456789 (9 km from SFCW-High), Rosalia WA 457180 (30 km from UICW-Intermediate), and La Crosse 3 ESE WA 454338 (27 km from WLCW-Low). Inverse-distance-weighting was used to fill in any missing data, using values from other stations within 100 km of the aforementioned three stations. Daily precipitation and minimum (Tmin) and maximum temperatures (Tmax) were aggregated to monthly, seasonal, and yearly values, and descriptive statistics were obtained. Climate data were divided into the “past” (1940–1982), during which the Kaiser data were collected, and the “present” (1983–2020), during which conservation farming has increasingly been adopted. The climate data within these two time periods were analyzed for:
- the number of precipitation events exceeding specified thresholds in each season,
- the number of freeze-thaw cycles and rain-on-thawing-soil events for each winter, with a thawing (frozen) soil defined as having frozen water in the soil profile less (more) than the previous day, and a complete freeze-thaw cycle defined as the soil surface changing from unfrozen to frozen and back again, and
- (a) normality (Shapiro-Wilkes test), (b) change in means (annual average precipitation and average daily Tmax and Tmin with two-sample Student’s t-test or Wilcoxon rank-sum test), and (c) linear trends (climate data in the past and present combined with linear regression or Mann Kendall test) in R (R Core Team, 2022), with parametric tests applied for normally-distributed data and non-parametric tests for non-normal distributions.
WEPP Simulations
The three model watersheds were initially selected using StreamStats (USGS, 2019), and their outlet information was used for watershed delineation in the WEPPcloud interface (Lew et al., 2022). Soil inputs were built for each hillslope, and WEPP was run to create a WEPPcloud project with detailed slope configuration and soil profile. This process was repeated to discretize the three model watersheds (table 1). The three WEPPcloud projects were downloaded. Python scripts were developed to refine inputs, including dividing the default single Overland Flow Element (OFE) into two to better simulate saturation-excess runoff at the lower part of the hillslope (Boll et al., 2015; Brooks et al., 2015), which tends to occur near the bottom of a hillslope. WEPP simulation was again conducted with revised inputs using the utility program wepppy (Lew et al. 2021; https://github.com/rogerlew/wepppy), with Python scripts that execute command-prompt WEPP for each model watershed.
| Table 1. Watershed discretization. | |||
| Area (ha) | Number of Hillslopes | Number of Channel Segments | |
| WLCW-Low | 8094 | 1632 | 721 |
| UICW-Intermediate | 3602 | 801 | 341 |
| SFCW-High | 5261 | 1163 | 507 |
WEPP Model Inputs
Climate
Six climate input files were created for the combination of two periods and three precipitation zones. The daily observed precipitation and temperature data for the three weather stations (near Pullman, Rosalia, and La Crosse) were used along with additional WEPP climate inputs. Event characteristics (duration, time to peak, peak intensity), dew-point temperature, and wind speed and direction were generated using CLIGEN 5.3 (Nicks et al., 1995; Srivastava et al., 2019) due to a lack of observed data, breakpoint, or other forms of fine temporal resolution (e.g., 5-min), which often do not match the actual weather patterns and could be a potential source of error.
Slope
SFCW-High and UICW-Intermediate are dominated by rolling hills, with 53% and 57% of the area falling in the 10–15% slope steepness, respectively (fig. 2). In contrast, WLCW-Low is flatter, with slopes <10% and 10–15% covering 61% and 26% of the area, respectively. Delineated hillslopes shorter than 50 m or longer than 300 m account for less than 14% in each of the three watersheds. Hillslopes 100–200 m long are the most common, covering 39% of WLCW-Low, 56% of UICW-Intermediate, and 52% of SFCW-High. Each hillslope was divided into two OFEs of equal length. Slope length was limited to 200 m, the maximum value in Meyer (1982) reporting soil losses measured on experimental plots of varying slope lengths across the United States.
Soil
The soils in both UICW-Intermediate and SFCW-High tend to be deep, with 100% and 92% >1200 mm, whereas only 41% of the soils in WLCW-Low are >1200 mm, and 55% are 800–1200 mm deep. Hillslope soil inputs were extracted from SSURGO and further synthesized and computed based on literature (e.g., WEPP Technical Documentation; Flanagan and Nearing, 1995) within WEPPcloud. Key hydraulic and erosion properties of the soil, namely the baseline hydraulic conductivity (Kb, mm hr-1), interrill erodibility (Ki,kg s m-4), rill erodibility (Kr, s m-1), and critical shear (tc, N m-2), were adjusted following the WEPP User Summary (Flanagan and Livingston, 1995) and applied following Dahal et al. (2022) (table 2).
Management
A single crop rotation of winter wheat-fallow with 100% intense tillage was used for the past (1940–1982), following McCool and Roe (2005). For the present (1983–2020), crop rotations vary by precipitation zone, and tillage practices were comprised of intense-, reduced-, and no-till with their annual percentages interpolated or extrapolated from county-level data reported by USDA NASS (2017) following Dahal et al. (2022). Because there was no tillage information by precipitation zone, the county-level tillage areal percentages were assumed for each watershed. Management input parameters for various tillage operations and specific crops for each rotation-tillage combination were based on Dahal et al. (2022); an example is presented in table 3.
Simulations Scenarios
WEPP watershed (v. 2020.5) simulations were performed for the two time periods and three watersheds, with crop rotations and tillage intensities appropriate to the simulation periods and precipitation zones (table 4). To account for the interactions between climatic and crop management conditions, we ran WEPP for each crop rotation with all possible starting phases. For example, for the first year of simulation for the present (1983), each of the three phases in the wheat-barley-pea (WBP) rotation could have been present in the field. Accordingly, this rotation was simulated three times by starting the crop rotation differently, resulting in WBP, BPW, and PWB (table 4, scenarios 24, 27, and 30). Likewise, WEPP runs with the past cropping system (wheat-fallow rotation and intense tillage) and the present climate were conducted starting with each phase for SFCW-High and UICW-Intermediate watersheds (table 4, scenarios 20, 21, 33, and 34) to elucidate the erosion effect of climate.
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| Figure 2. Variation of percent slope gradient (a: WLCW-Low, b: UICW-Intermediate, c: SFCW-High) and slope length in m (d, e, f) across the selected watersheds. |
Analysis of WEPP Simulation Results
Aggregation
Annual (water years from 1 October to 30 September of the following year) and monthly water balance and erosion results were aggregated for all hillslopes within each watershed for each scenario. Channel erosion was not examined in this study. The results from the scenarios testing different starting phases of the same crop rotation (e.g., scenarios 24, 27, and 30, table 4) were averaged (assuming equal areal fraction) for each combination of tillage type, watershed, and simulation period. Subsequently, the averages for the three tillage types for each model watershed and period were area-weighted based on Whitman County tillage type proportions projected from data reported in USDA NASS (2017) as described by Dahal et al. (2022).
A statistical comparison (a = 0.05) of the mean annual erosion rates was made for the two periods using either two-sample t-tests or the Wilcoxon rank-sum test, depending on the normality of the data. Correlation analysis (a = 0.05) of event and annual erosion rate (Mg ha-1), water input (rain + melt) and runoff (mm), winter conditions, and hillslope properties was conducted for WLCW-Low, UICW-Intermediate, and SFCW-High for the “worst case” intense tillage scenarios.
| Table 2. Major soil inputs for a predominantly deep soil (Palouse silt loam). | ||
| Soil Inputs | Value | |
| Texture | Silt loam | |
| Soil name | Palouse Silt Loam | |
| Mukey | 68563 | |
| Albedo | 0.16 | |
| Initial saturation of soil, m3 m-3 | 0.4 | |
| Baseline interrill erodibility Ki, kg s m-4 | 9.8×106 | |
| Baseline rill erodibility Kr, s m-1 | 0.0178 | |
| Baseline critical shear tc, N m-2 | 0.35 | |
| Depth to the restrictive layer, m | 1.5 | |
| Restrictive layer hydraulic conductivity, mm hr-1 | 0.00072 | |
Table 3. Major management inputs for wheat under intense tillage in a wheat-barley-pea rotation. | ||
| Management Inputs | Value | |
| Ridge height value after tillage, m | 0.075 | |
| Ridge interval, m | 0.30 | |
| Fraction of residue buried by chisel plow[a] | 0.65 | |
| Fraction of residue buried by moldboard plow[a] | 0.80 | |
| Depth of tillage for chisel and moldboard plow, m | 0.20 | |
| Depth of tillage for spike tooth harrow, m | 0.076 | |
| Random roughness value after spike tooth harrowing, m | 0.025 | |
| Fraction of surface area disturbed | 1.0 | |
| Row width, m | 0.18 | |
| Maximum canopy height for winter wheat, m | 1.0 | |
| Canopy cover coefficient for winter wheat | 5.2 | |
| Initial ridge height after last tillage, m | 0.08 | |
| Initial ridge roughness after last tillage, m | 0.05 | |
| Initial snow depth, m | 0.0 | |
| Initial frost depth, m | 0.0 | |
| Initial dead root mass, kg m-2 | 0.4 | |
[a]From table 9.5.1 of WEPP documentation (Stott et al., 1995) | ||
Comparison with Kaiser Field Data
WEPP-simulated annual erosion results for the past were compared with the historical (Kaiser) annual field erosion data reported in McCool and Roe (2005). There is a lack of documentation in the literature of the specific fields examined in the Kaiser study, particularly their locations, even upon review of WSU library records of the survey archives (Kaiser, 2021). The fields denoted “sites with a long history of observation” during the study had township and range information (Kaiser, 2021), which was digitized and mapped (fig. 1). Approximately 66% of these fields were in the high-precipitation zone, and 33% were in the intermediate-precipitation zone. Based on this proportion, we area-weighted the WEPP-simulated annual erosion results for the high- and intermediate-precipitation zones and compared the results with the Kaiser data.
| Table 4. WEPP simulation scenarios. | |||||||||||||
| Winn Lake Canyon Watershed (WLCW-Low) | Upper Imbler Creek Watershed (UICW-Intermediate) | Spring Flat Creek Watershed (SFCW-High) | |||||||||||
| Scenario | Period | Rotation[a] | Tillage | Scenario | Period | Rotation[a] | Tillage | Scenario | Period | Rotation[a] | Tillage | ||
| 1 | Past | WF | Intense | 9 | Past | WF | Intense | 22 | Past | WF | Intense | ||
| 2 | FW | Intense | 10 | FW | Intense | 23 | FW | Intense | |||||
| 3 | Present | WF | Intense | 11 | Present | WBF | Intense | 24 | Present | WBP | Intense | ||
| 4 | Reduced | 12 | Reduced | 25 | Reduced | ||||||||
| 5 | No-till | 13 | No-till | 26 | No-till | ||||||||
| 6 | FW | Intense | 14 | BFW | Intense | 27 | BPW | Intense | |||||
| 7 | Reduced | 15 | Reduced | 28 | Reduced | ||||||||
| 8 | No-till | 16 | No-till | 29 | No-till | ||||||||
| 17 | FWB | Intense | 30 | PWB | Intense | ||||||||
| 18 | Reduced | 31 | Reduced | ||||||||||
| 19 | No-till | 32 | No-till | ||||||||||
| 20 | WF | Intense | 33 | WF | Intense | ||||||||
| 21 | FW | Intense | 34 | FW | Intense | ||||||||
[a]W = wheat, B = barley, P = pea, and F = fallow. | |||||||||||||
Results and Discussion
Climate Characteristics
Mean Comparison
Average annual precipitation increased from the past to the present by 24 mm in WLCW-Low and decreased by 41 mm in SFCW-High. In UICW-Intermediate, the 3 mm decrease is negligible (table 5). The average daily temperatures (Tmax, Tmin) are greater in the present than in the past, except for the average daily Tmin in WLCW-Low. All increases in average daily temperatures except Tmin in UICW-Intermediate were significant per the Wilcoxon rank-sum test (table 5). The number of average annual rain-on-thawing-soil events increased significantly from 19 to 23 in WLCW-Low and decreased significantly from 25 to 21 and 29 to 24 in UICW-Intermediate and SFCW-High, respectively.
Long-Term Trend
Changes in annual precipitation were not significant (table 5, fig. 3). Tmax and Tmin in SFCW-High and Tmax in UICW-Intermediate have increased significantly, and long-term rain-on-thawing-soil events increased significantly in WLCW-Low and decreased significantly in UICW-Intermediate and SFCW-High (table 5, fig. 3).
WEPP Simulation Results
Water Balance
WEPP-simulated water balance for the three watersheds, WLCW-Low, UICW-Intermediate, and SFCW-High, changed from the past to the present, with change primarily due to changes in climatic conditions, or to changes in both climatic conditions and cropping systems (table 6). In WLCW-Low, the cropping system (wheat-fallow rotation, intense tillage) has remained the same. However, the 7.3% increase in average annual precipitation increased the amount of every water balance component: soil water, runoff, and ET, as well as lateral flow and deep percolation (together accounting for ~2%).
In UICW-Intermediate, changes in water balance are mainly due to changes in crop rotation, as average annual water input (rain + snowmelt) decreased by only 0.4%. For the intense tillage practice, the switch from the past wheat-fallow to a wheat-barley-fallow rotation increases annual crop consumptive use and total ET. The increase in ET in turn decreases soil water, which enhances infiltration and reduces runoff. These patterns also hold true under reduced- and no-till, which allowed even more ET and less runoff than intense tillage.
In SFCW-High, precipitation decreasing by 7.3% from past to present decreases the average annual runoff. For intense tillage, shifting from wheat-fallow to the three-year wheat-barley-pea rotation increases annual crop consumptive use and ET (table 6), just as it did for UICW-Intermediate. The increase in ET decreases soil water, enhances infiltration, and decreases runoff.
For all three watersheds within the present time period, a decrease in tillage intensity increases the amount of surface residue and resistance to surface water flow (Gilley and Weltz, 1995), thus decreasing runoff and increasing soil water and ET. The increase in ET removes more soil water, which allows more infiltration and, in turn, decreases runoff in a “virtuous” cycle.
Annual Erosion
Area-weighted average annual simulated erosion decreased from the past by 32%, 57%, and 70% for the WLCW-Low, UICW-Intermediate, and SFCW-High watersheds, respectively. Area-weighting was based on the average erosion rates for intense-, reduced-, and no-till based on their present percent areas for Whitman County (table 6). The decrease was not significant for WLCW-Low (W = 998, p = 0.09) but was significant for UICW-Intermediate (W = 1280, p < 0.0001) and SFCW-High (W = 1484, p < 0.0001) per the Wilcoxon rank-sum test. The non-parametric test was used because average annual erosion rates were non-normal for all three watersheds.
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| Figure 3. Temporal variation of (a, b, c) annual precipitation and (d, e, f) means of daily maximum and minimum temperatures for the study watersheds WLCW-Low, UICW-Intermediate, and SFCW-High, respectively. |
| Table 5. Comparison of means and long-term trends: average annual precipitation (P, mm), average daily maximum (Tmax) and minimum (Tmin) temperatures (°C), number of rain-on-thawing-soil events (RT), and freeze-thaw cycles (FT) of the past and present with p-values in parentheses. Non-parametric, Wilcoxon rank-sum and Mann Kendall tests were conducted for non-normal distributions. Significant tests (at a = 0.05) are in bold face. | |||||||||
| Mean Comparison | Long-Term Trend | ||||||||
| Normality Test | t-Test | Wilcoxon Rank- Sum Test | Linear Reg. Slope | Mann Kendall Test | |||||
| Watershed | Past | Present | W[a] (p) | T (p) | w[b] (p) | ß[c] (p) | t[d] (p) | ||
| WLCW[e] -Low | P | 353 | 377 | 0.99 (0.590) | -1.28 (0.205) | 0.43 (0.280) | |||
| Tmax | 16.9 | 17.1 | 0.85 (<0.0001) | 638 (0.045) | 0.122 (0.103) | ||||
| Tmin | 2.9 | 2.7 | 0.94 (<0.0001) | 941 (0.454) | -0.093 (0.214) | ||||
| FT | 15 | 16 | 0.98 (0.25) | -0.92 (0.36) | 0.02 (0.241) | ||||
| RT | 19 | 23 | 0.95 (0.010) | 535 (0.008) | 0.247 (0.001) | ||||
| UICW[f] -Intermediate | P | 458 | 455 | 0.98 (0.410) | 0.11 (0.911) | -0.15 (0.762) | |||
| Tmax | 14.4 | 14.9 | 0.91 (<0.0001) | 498 (0.001) | 0.240 (0.001) | ||||
| Tmin | 2.3 | 2.5 | 0.97 (0.050) | 743 (0.298) | -0.043 (0.571) | ||||
| FT | 16 | 15 | 0.97 (0.090) | 0.92 (0.359) | 0.01 (0.584) | ||||
| RT | 25 | 21 | 0.97 (0.040) | 1064 (0.020) | -0.138 (0.08) | ||||
| SFCW[g] -High | P | 552 | 511 | 0.98 (0.110) | 1.75 (0.084) | -0.78 (0.117) | |||
| Tmax | 14.2 | 14.8 | 0.91 (<0.0001) | 474 (<0.0001) | 0.228 (0.002) | ||||
| Tmin | 2.6 | 2.9 | 0.96 (0.020) | 557 (0.006) | 0.196 (0.009) | ||||
| FT | 17 | 16 | 0.98 (0.12) | 0.064 (0.949) | 0.01 (0.695) | ||||
| RT | 29 | 24 | 0.98 (0.40) | 2.99 (0.004) | -0.09 (0.021) | ||||
[a]W = normality test statistic. [b] w = Wilcoxon rank-sum test statistic. [c]ß = linear regression slope. [d]t = Mann Kendall test statistic. [e] WLCW = Winn Lake Canyon Watershed in the low-precipitation zone. [f] UICW = Upper Imbler Creek Watershed in the intermediate-precipitation zone. [g] SFCW = Spring Flat Creek Watershed in the high-precipitation zone. | |||||||||
For WLCW-Low, under a consistent wheat-fallow rotation and intense tillage, the erosion rate decreased by 1.5% (not significant; W = 802, p = 0.891) from the past to the present due to changes in climate (more detail in 3.2.2.1). Erosion rates under intense tillage decreased significantly from the past to the present by 34% for UICW-Intermediate (W = 1065, p = 0.019) and by 49% for SFCW-High (W = 1294, p < 0.0001). For the former, this is because runoff decreased due to the change in rotation from wheat-fallow to wheat-barley-fallow. For the latter, the even greater decrease in erosion is due both to a similar change to a three-year crop rotation and to decreased average annual precipitation.
The simulated erosion rate also decreases with a decrease in tillage intensity, as the reduced- and no-till systems have more surface crop residues, which increase both infiltration and resistance to water flow and soil detachment. Overall, the WEPP-simulated erosion rates for all three watersheds decreased from the past to the present due to the combined effects of changes in climatic conditions and management practices, as elaborated in the next section.
| Table 6. Average annual water balance and erosion (with percentages of annual water balance outputs in parentheses) for the WLCW-Low, UICW-Intermediate, and SFCW-High study watersheds. Values are averages of results with different starting phases of crop rotation. | ||||||||
| Watershed | Period | Tillage | Water Balance (mm) | Erosion (Mg ha-1) | ||||
| Rain + Snowmelt | Runoff | ET | LF + DP[a] | Soil Water[b] | ||||
| WLCW- Low | Past | Intense | 357 | 32 (9) | 315 (88) | 6 (2) | 180 | 13.5 |
| Present | Intense | 383 | 41 (11) | 333 (87) | 7 (2) | 190 | 13.3 | |
| Reduced | 31 (8) | 343 (89) | 8 (2) | 198 | 5.8 | |||
| No-till | 29 (7) | 346 (90) | 8 (2) | 198 | 2.3 | |||
| Area-weighted[c] | 9.5 | |||||||
| UICW- Intermediate | Past | Intense | 464 | 87 (19) | 368 (79) | 5 (1) | 309 | 34.5 |
| Present | Intense | 462 | 77 (17) | 383 (83) | 3 (1) | 270 | 22.9 | |
| Reduced | 59 (13) | 400 (86) | 3 (1) | 288 | 8.0 | |||
| No-till | 57 (12) | 402 (87) | 3 (1) | 286 | 2.7 | |||
| Area-weighted | 14.1 | |||||||
| SFCW- High | Past | Intense | 559 | 128 (23) | 406 (73) | 15 (3) | 426 | 52.6 |
| Present | Intense | 518 | 93 (18) | 423 (82) | 1 (0) | 279 | 27.0 | |
| Reduced | 59 (11) | 454 (87) | 5 (1) | 324 | 5.7 | |||
| No-till | 55 (11) | 457 (88) | 5 (1) | 324 | 2.2 | |||
| Area-weighted | 15.5 | |||||||
[a] Lateral flow and deep percolation. [b] Averaged daily. [c]Area-weighted average annual erosion based on percent areas of intense-, reduced-, and no-till in Whitman County. | ||||||||
Climate Effect
Within any given crop rotation and tillage practice, WEPP-simulated erosion rates decreased from the past for all three watersheds. For WLCW-Low, the mean erosion rate for the past was 13.5 Mg ha-1 compared to the present 13.3 Mg ha-1 (table 5). The simulation results differed according to the phase of the rotation. Starting the rotation with wheat yielded a decreased erosion rate at 12.6 Mg ha-1 compared to the past 14.3 Mg ha-1, whereas starting with fallow led to an increased erosion rate at 14.0 Mg ha-1 compared to the past 12.7 Mg ha-1. This discrepancy was due to a climate-rotation interaction: in the second scenario, the years with the highest annual precipitation, 1995 and 1997, coincide with wheat years, resulting in greater erosion in contrast to the first scenario, in which these two years were fallow with lower erosion. For UICW-Intermediate and SFCW-High under intense tillage, annual erosion rates averaged over WF and FW were respectively 34.5 and 52.6 Mg ha-1 for the past, compared to 26.7 and 35.0 Mg ha-1 for the present.
For UICW-Intermediate, the decrease in annual precipitation by 0.7%, winter (Dec–Feb) precipitation by 5%, and the number of precipitation events greater than 15 mm (hereafter “large precipitation events”) by 25% led to a lower simulated average annual erosion. In SFCW-High, the average annual precipitation, winter precipitation, and the number of large precipitation events all decreased from the past to the present (table 5, fig. 4). This shift in precipitation patterns decreased the present simulated average annual runoff and erosion.
 |
| Figure 4. Distribution of size of precipitation events for the past and present. Bars represent event frequency, whereas values above the bars are averaged counts of annual precipitation events. (a) WLCW-Low, (b) UICW-Intermediate, and (c) SFCW-High study watersheds. |
Management Effect
Tillage Effect
Decreasing tillage intensity (fewer tillage passes, shallower tillage, and less residue buried) decreases simulated erosion rates for the same crop rotation and climate conditions. Incorporating conservation tillage practices in the present based on projected percent areas with conservation tillage in Whitman County led to a decrease in average annual erosion by 31%, 35%, and 40% in WLCW-Low, UICW-Intermediate, and SFCW-High, respectively, compared to the scenario where all the areas in the watershed remained under intense tillage (table 5).
Crop Rotation Effect
The shift from wheat-fallow in the past to wheat-based three-year crop rotations in the present in UICW-Intermediate and SFCW-High contributes substantially to decreased erosion (fig. 5). WEPP-simulated average annual erosion rates under wheat-fallow for these two zones were 26.7 and 35.0 Mg ha-1, both above those for wheat-barley-pea and wheat-barley-fallow, regardless of the starting phase of the rotations.
Wheat and fallow years tend to have high erosion rates. During wheat years, the winter rainy season coincides with low crop cover, as the late fall-planted crop may not have tillered yet. Additionally, in the intense tillage scenario, wheat requires multiple passes with intense pre-planting tillage in the fall, which increases soil erodibility. Fallow practices are employed to accumulate soil water; however, the increased soil water storage during the fallow year can reduce subsequent infiltration, leading to increased runoff and erosion.
 |
| Figure 5. Average monthly erosion by crop year for the past and present. (a–d) WLCW-Low, (e–h) UICW-Intermediate, and (i–l) SFCW-High. |
The “hypothetical” wheat-fallow rotation for the present (no longer practiced except in WLCW-Low) yielded an average daily soil water storage of 316 mm and 408 mm for UICW-Intermediate and SFCW-High, compared to 270 mm and 279 mm for the three-year (wheat-barley-fallow or wheat-barley-pea) rotations in their respective zones. In contrast, barley and pea, planted in mid-April after fewer tillage passes, provide crop cover, deplete soil water, allow more infiltration, and result in less runoff and erosion. In addition, barley and pea are harvested mid-August, followed by only one secondary tillage pass in October, leaving more residue cover in these crop years, whereas the soil surface is left bare during the fallow years. Starting the rotation with different crop phases for the same precipitation zone and time period yielded different year-by-year erosion results, ranging from 0 to 140 Mg ha-1. In most cases, large erosion events occur during the fallow or wheat year in combination with extreme precipitation events occurring shortly after tillage.
Comparison with Kaiser Field Data
WEPP-simulated average annual erosion for 1940–1982 was 45.3 Mg ha-1 for the area-weighted SFCW-High and UICW-Intermediate watersheds in Whitman County where Kaiser field data were collected, roughly 15% lower than the average of 53.8 Mg ha-1 of the Kaiser data reported in McCool and Roe (2005). WEPP-simulated annual erosion followed the trend observed for certain periods, including 1948–1979 (fig. 6a). However, disagreements exist in yearly comparisons (R2 = 0.02, RMSE = 22.7 Mg ha-1, fig. 6b). The discrepancies in both annual erosion rates and their average could be due to uncertainties in both our interpretation of the Kaiser data, and the WEPP simulations themselves. Specifically, erosion was surveyed at selected locations in the intermediate- and high-precipitation zones, while WEPP results represent the area weighting of model watersheds. Further, crop rotations and their phases, as well as tillage practices, likely varied from farm to farm during the study period. Due to the lack of such historical details, these complexities could not be replicated in the WEPP simulation. Lastly, we used CLIGEN-generated precipitation events, which differ from the actual events in duration, time to peak, and peak intensity. This could also introduce errors into the simulated erosion.
 |
 |
| Figure 6. Observed and WEPP-simulated average annual erosion rates (t ha–1) for the area-weighted UICW-Intermediate and SFCW-High watersheds where Kaiser field data were collected. (a) Yearly variation and (b) paired comparison. |
Simulations with different starting crop phases (wheat or fallow) yielded similar annual averages (45.3 Mg ha-1 for fallow-wheat, 45.9 Mg ha-1 for wheat-fallow), but different periods of better agreement in terms of temporal trend: starting with wheat vs. fallow produced a simulated trend more similar to the observed for 1949–1970 vs. 1971–1982 (not shown). In both cases, erosion peaked prominently during the wheat years for reasons elaborated in section 3.2.2.
Yearly fluctuations in the observed erosion during 1940–1982 followed the annual precipitation trend, except for the years 1941 and 1947. The observed annual erosion rates exceeded 100 Mg ha-1 for 1942, 1943, 1946, and 1963. WEPP-simulated values, however, were much lower, especially for 1942 and 1943. The observed erosion was greater than 150 Mg ha-1 for 1942 with an annual precipitation depth of 443 mm, which was lower by 90 mm than the average annual precipitation. During this year, a total of 114 precipitation events occurred (similar to the average of 113), but the number of precipitation events greater than 10, 15, and 20 mm were all considerably fewer than the yearly averages. There were only three large precipitation events, at 17, 27, and 29 mm. These average- or below-average precipitation conditions resulted in a WEPP-simulated erosion rate of 35 Mg ha-1 for the year. On the other hand, the observed erosion for 1947 was exceptionally low despite above-average annual precipitation, such that the WEPP simulated results were high in comparison. This difference could have resulted from the use of stochastically generated precipitation events. The observed low erosion in 1977 was likely due to the below-average precipitation for that year, which was partially reproduced by WEPP model simulations.
Temporal Trend
Erosion mostly occurs during the rainy season from November to March (fig. 5). WEPP-simulated erosion rates varied year by year, as both weather and cropping conditions also varied (fig. 7). A combination of above-average annual precipitation and a large number of rain-on-thawing-soil events led to high erosion rates in the years 1959, 1978, and 1995 for all three watersheds. The many precipitation events in 2016, especially rain-on-thawing-soil events, resulted in elevated erosion for both UICW-Intermediate and SFCW-High. That same year, lower-than-average winter precipitation and fewer rain-on-thawing-soil events in WLCW-Low resulted in low erosion. Further, low winter precipitation and fewer rain-on-thawing-soil events resulted in low erosion rates in the years 1993 and 2013 in all three watersheds.
 |
| Figure 7. Temporal variations in WEPP-simulated annual erosion from the past to present. Study watersheds and their precipitation zones are (a) WLCW-Low, (b) UICW-Intermediate, and (c) SFCW-High. The vertical dashed line separates the past and present periods. |
As expected, both event and annual erosion rates were significantly (a = 0.05) correlated with runoff (table 7). Likewise, runoff and erosion rates were significantly correlated with water input (rain + melt) for all three watersheds. The event erosion rate was positively correlated with Tmin, as greater temperatures in winter tend to cause soils to thaw and to be more erodible. The effect of the number of cumulative freeze-thaw cycles prior to an erosion event was significant for SFCW-High but not for the other two watersheds. The annual erosion rate was correlated with the number of rain-on-thawing-soil events in WLCW-Low and SFCW-High, but not in UICW-Intermediate. There was no significant correlation between the annual erosion rate and the number of freeze-thaw cycles (table 7) in any of the three watersheds, corroborating the findings of McCool et al. (2006).
| Table 7. Pearson correlation analysis of event and annual erosion rate (Mg ha-1), water input (rain + melt) and runoff (mm), and winter conditions for the study watersheds WLCW-Low, UICW-Intermediate, and SFCW-High. Non-significant (at a = 0.05) tests are italicized, and p-values are shown in parentheses. | ||||||
| Watershed | Erosion | Rain + Melt | Runoff | Cum. FT/FT[a] | ||
| Event | WLCW- Low | Rain + Melt | 0.51 (<0.001) | |||
| Runoff | 0.67 (<0.001) | 0.69 (<0.001) | ||||
| Cum. FT | -0.03 (0.295) | 0.09 (<0.001) | -0.11 (<0.001) | |||
| Tmin[b] | 0.14 (<0.001) | 0.26 (<0.001) | -0.004 (0.865) | 0.22 (<0.001) | ||
| UICW- Intermediate | Rain + Melt | 0.52 (<0.001) | ||||
| Runoff | 0.58 (<0.001) | 0.81 (<0.001) | ||||
| Cum. FT | -0.03 (0.258) | 0.17 (<0.001) | -0.07 (0.002) | |||
| Tmin | 0.27 (<0.001) | 0.38 (<0.001) | 0.12 (<0.001) | 0.31 (<0.001) | ||
| SFCW- High | Rain + Melt | 0.44 (<0.001) | ||||
| Runoff | 0.51 (<0.001) | 0.69 (<0.001) | ||||
| Cum. FT | -0.04 (0.008) | 0.09 (<0.001) | -0.10 (<0.001) | |||
| Tmin | 0.21 (<0.001) | 0.32 (<0.001) | 0.02 (0.27) | 0.24 (<0.001) | ||
| Annual | WLCW- Low | Rain + Melt | 0.41 (<0.001) | |||
| Runoff | 0.69 (<0.001) | 0.42 (<0.001) | ||||
| FT | -0.1 (0.227) | -0.32 (<0.001) | 0.05 (<0.001) | |||
| Rain-on-Thawing Soil | 0.23 (0.004) | 0.10 (0.198) | 0.37 (<0.001) | 0.25 (0.001) | ||
| UICW- Intermediate | Rain + Melt | 0.22 (<0.001) | ||||
| Runoff | 0.45 (<0.001) | 0.55 (<0.001) | ||||
| FT | -0.11 (0.088) | -0.14 (0.03) | 0.13 (0.053) | |||
| Rain-on-Thawing Soil | 0.08 (0.218) | 0.22 (0.0005) | 0.37 (<0.001) | 0.27 (<0.001) | ||
| SFCW- High | Rain + Melt | 0.35 (<0.001) | ||||
| Runoff | 0.53 (<0.001) | 0.57 (<0.001) | ||||
| FT | -0.03 (0.693) | -0.01 (0.88) | 0.18 (0.006) | |||
| Rain-on-Thawing Soil | 0.20 (0.002) | 0.33 (<0.001) | 0.53 (<0.001) | 0.36 (<0.001) | ||
[a]Cum. FT = cumulative freeze-thaw cycles prior to an erosion event, for event analysis; FT = total number of freeze-thaw cycles in a water year, for annual analysis. [b]Tmin, minimum air temperature. | ||||||
Spatial Variation
WEPP-simulated average annual erosion rates vary spatially; the higher rates are associated with hillslopes with greater slope length and steepness (table 8). In SFCW-High, the annual erosion rate was negatively correlated with soil depth, as deeper soils have larger storage and thus lower potential for runoff and erosion, consistent with findings of previous studies (Brooks et al., 2015; Dahal et al., 2022). In contrast, for WLCW-Low, the annual erosion rate was positively correlated with soil depth, likely because of the combined effects of confounding factors. Hillslopes with deeper soil in this watershed tend to be longer and steeper, with slightly lower saturated hydraulic conductivity and higher interrill and rill erodibilities. No analysis was made between erosion rate and soil depth for UICW-Intermediate because of the lack of variation of soil depth in this watershed.
Areas with simulated erosion rates below the NRCS tolerable limit (<11 Mg ha-1 yr-1) increased from the past to the present in all three watersheds, and areas with erosion rates far exceeding the tolerable limit (>30 Mg ha-1 yr-1) decreased from the past (table 9, fig. 8). Nonetheless, areas with simulated erosion rates above the tolerable limit still account for 34%, 40%, and 39% of the total areas in WLCW-Low, UICW-Intermediate, and SFCW-High, respectively.
| Table 8. Pearson correlation between annual average erosion and hillslope properties for the WLCW-Low, UICW-Intermediate, and SFCW-High watersheds. Non-significant (at a = 0.05) tests are italicized, and p-values are shown in parentheses. | ||||
| Erosion | Slope Length | Gradient | ||
| WLCW- Low | Slope Length | 0.32 (<0.001) | ||
| Gradient | 0.59 (<0.001) | -0.03 (-0.03) | ||
| Soil Depth | 0.29 (<0.001) | 0.01 (-0.59) | 0.11 (<0.001) | |
| UICW- Intermediate | Slope Length | 0.42 (<0.001) | ||
| Gradient | 0.41 (<0.001) | -0.22 (<0.001) | ||
| Soil Depth[a] | ||||
| SFCW- High | Slope Length | 0.35 (<0.001) | ||
| Gradient | 0.41 (<0.001) | 0.10 (<0.001) | ||
| Soil Depth | -0.19 (<0.001) | 0.02 (0.21) | 0.26 (<0.001) | |
[a]No analysis for this watershed, which has only one soil depth. | ||||
| Table 9. Changes in percent areas of erosion from past to present in the WLCW-Low, UICW-Intermediate, and SFCW-High watersheds. | |||||
| Areas with Erosion Rate <11 Mg ha-1 (%) | Areas with Erosion Rate > 30 Mg ha-1 (%) | ||||
| Past | Present | Past | Present | ||
| WLCW-Low | 52 | 66 | 10 | 4 | |
| UICW-Intermediate | 4 | 60 | 68 | 3 | |
| SFCW-High | 4 | 60 | 82 | 3 | |
Conclusions
In this study, we applied WEPP (v. 2020.5) to assess the temporal trend of water erosion across three precipitation zones in Whitman County, eastern Washington State. We separated the climate record into the past (1940–1982) and the present (1983–2020). By delineating a watershed within each zone, we conducted WEPP model simulations over these two periods, analyzing changes under various tillage practices and crop rotations. Our analysis of climatic parameters, specifically annual precipitation and daily maximum and minimum temperatures, alongside WEPP simulation results, highlighted notable long-term trends. Additionally, WEPP-simulated annual erosion rates for the past were compared with the Kaiser field data. Our major conclusions from this study are:
- Climate Trends:Daily maximum and minimum temperatures increased from the past in all three watersheds except Tmin in the WLCW-Low. The increase in temperature was statistically significant except for Tmin in UICW-Intermediate. The number of rain-on-thawing-soil events increased significantly in WLCW-Low and decreased significantly in UICW-Intermediate and SFCW-High.
- Erosion: Past vs. Present:WEPP-simulated average annual erosion rates decreased from the past to present by 32%, 57%, and 70% for the study watersheds in the low-, intermediate-, and high-precipitation zones, respectively. The decreases were due to the combined effects of changing climatic patterns and management practices.
- Climatic Effect:The decrease in annual precipitation (particularly winter), and the number of large precipitation events were the key climatic factors (or parameters) that led to a decrease in erosion.
- Management Effect: The shift from two-year to three-year crop rotations, coupled with the shift from intensive tillage to conservation tillage practices, were the pivotal management changes that led to a reduction in erosion. Incorporating reduced- and no-till decreased WEPP-simulated average annual erosion by 31%, 35%, and 40% in WLCW-Low, UICW-Intermediate, and SFCW-High, respectively.
- Comparison with Kaiser Field Data:WEPP-simulated average annual erosion rate agreed with the Kaiser field data. WEPP reproduced year-by-year variations for certain periods, especially 1948 to 1979.
- Temporal and Spatial Assessment:Erosion varied year to year, driven by the annual precipitation events, the number of precipitation events, and rain-on-thawing-soil events. Spatially, the areas with erosion rates below the NRCS tolerable limit increased from the past to the present in all three watersheds. Yet 34%, 40%, and 39% of the total areas in WLCW-Low, UICW-Intermediate, and SFCW-High watersheds, respectively, still generate erosion exceeding the NRCS tolerance limit of 11 Mg ha-1 yr-1.
- Correlation with Climate Parameters: Erosion events were positively correlated with annual water input, runoff, and minimum temperature for all three watersheds. The number of annual freeze-thaw cycles was not correlated with annual erosion rates in the three watersheds. The number of annual rain-on-thawing-soil events was significantly correlated with annual erosion rates for the watersheds in the low- and high-precipitation zones.
- Correlation with Spatial Parameters: The average annual erosion rate was significantly correlated with hillslope length and steepness for all three watersheds. The average annual erosion rate in SFCW-High was negatively correlated with soil depth, as deep soils have a lower potential for runoff and erosion. However, the erosion rate was positively correlated with soil depth in WLCW-Low, because hillslopes with deeper soils in this watershed tend to be longer and steeper, with lower hydraulic conductivity and higher erodibility.
- Limitations: The study is limited by the lack of observed characteristics of precipitation events, which likely contributed to differences between WEPP-simulated and observed erosion. The use of county-level tillage practice data for subareas within the county has likely also contributed to the simulation errors.
 |
| Figure 8. Average annual WEPP-simulated erosion rates (Mg ha-1) for the (a, b, c) past and the (d, e, f) present in the WLCW-Low, UICW-Intermediate, and SFCW-High watersheds. |
Acknowledgments
This study is in part supported by USDA AFRI NIFA (Grant No. 2018-68002-27920). We thank Dr. Markus Flury for suggesting the comparison between WEPP results and Kaiser field data. We thank Mr. Stephen Johnson, USDA NRCS, for providing information on common crop rotations and tillage practices in the study area. We are grateful to the two anonymous reviewers and the editor for their constructive comments, which improved the rigor and clarity of the article.
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